R Graphics Cookbook Chapter 5
Mitchell Holman
2018-04-17
Chapter 5. Scatter Plots
library(ggplot2)
library(gcookbook)
library(plyr)
library(gridExtra)
library(knitr)
library(hexbin)
library(MASS)Section 5.1. Making a Basic Scatterplot
ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point()
five1 <- ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=21)
five2 <- ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(size=1.5)
grid.arrange(five1, five2, ncol = 2)
Section 5.2. Grouping Data Points by a Variable Using Shape or Color
c5.21<- ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) + geom_point()
c5.22 <- ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex)) + geom_point()
grid.arrange(c5.21, c5.22, ncol= 2)
ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex)) +
geom_point() +
scale_shape_manual(values=c(1,2)) +
scale_colour_brewer(palette="Set1")
Section 5.3. Using Different Point Shapes
ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=3)
ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex)) +
geom_point(size=3) + scale_shape_manual(values=c(1, 4))
hw <- heightweight
hw$weightGroup <- cut(hw$weightLb, breaks=c(-Inf, 100, Inf),
labels=c("< 100", ">= 100"))
ggplot(hw, aes(x=ageYear, y=heightIn, shape=sex, fill=weightGroup)) +
geom_point(size=2.5) +
scale_shape_manual(values=c(21, 24)) +
scale_fill_manual(values=c(NA, "black"),
guide=guide_legend(override.aes=list(shape=21)))
Section 5.4. Mapping a Continuous Variable to Color or Size
ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=weightLb)) + geom_point()
ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb)) + geom_point()
ggplot(heightweight, aes(x=ageYear, y=heightIn, fill=weightLb)) +
geom_point(shape=21, size=2.5) +
scale_fill_gradient(low="black", high="white")
ggplot(heightweight, aes(x=ageYear, y=heightIn, fill=weightLb)) +
geom_point(shape=21, size=2.5) +
scale_fill_gradient(low="black", high="white", breaks=seq(70, 170, by=20),
guide=guide_legend())
ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb, colour=sex)) +
geom_point(alpha=.5) +
scale_size_area() +
scale_colour_brewer(palette="Set1")
Section 5.5 Dealing with Overplotting
sp <- ggplot(diamonds, aes(x=carat, y=price))
sp + geom_point()
sp + geom_point(alpha=.1)
sp + geom_point(alpha=.01)
sp + stat_bin2d()
sp + stat_bin2d(bins=50) +
scale_fill_gradient(low="lightblue", high="red", limits=c(0, 6000))
sp + stat_binhex() +
scale_fill_gradient(low="lightblue", high="red",
limits=c(0, 8000))
sp + stat_binhex() +
scale_fill_gradient(low="lightblue", high="red",
breaks=c(0, 250, 500, 1000, 2000, 4000, 6000),
limits=c(0, 6000))
sp1 <- ggplot(ChickWeight, aes(x=Time, y=weight))
sp1 + geom_point()
sp1 + geom_point(position="jitter")
sp1 + geom_point(position=position_jitter(width=.5, height=0))
sp1 + geom_boxplot(aes(group=Time))
Section 5.6. Adding Fitted Regression Model Lines
sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn))
sp + geom_point() + stat_smooth(method=lm)
sp + geom_point() + stat_smooth(method=lm, level=0.99)
sp + geom_point() + stat_smooth(method=lm, se=FALSE)
sp + geom_point(colour="grey60") +
stat_smooth(method=lm, se=FALSE, colour="black")
sp + geom_point(colour="grey60") + stat_smooth()
sp + geom_point(colour="grey60") + stat_smooth(method=loess)
b <- biopsy
b$classn[b$class=="benign"] <- 0
b$classn[b$class=="malignant"] <- 1
ggplot(b, aes(x=V1, y=classn)) +
geom_point(position=position_jitter(width=0.3, height=0.06), alpha=0.4,
shape=21, size=1.5) +
stat_smooth(method=glm, family=binomial)
sps <- ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) +
geom_point() +
scale_colour_brewer(palette="Set1")
sps + geom_smooth()
sps + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
Section 5.7. Adding Fitted Lines from an Existing Model
model <- lm(heightIn ~ ageYear + I(ageYear^2), heightweight)
xmin <- min(heightweight$ageYear)
xmax <- max(heightweight$ageYear)
predicted <- data.frame(ageYear=seq(xmin, xmax, length.out=100))
predicted$heightIn <- predict(model, predicted)
predicted ageYear heightIn
1 11.58000 56.82624
2 11.63980 57.00047
3 11.69960 57.17294
4 11.75939 57.34363
5 11.81919 57.51255
6 11.87899 57.67969
7 11.93879 57.84507
8 11.99859 58.00867
9 12.05838 58.17051
10 12.11818 58.33056
11 12.17798 58.48885
12 12.23778 58.64537
13 12.29758 58.80011
14 12.35737 58.95308
15 12.41717 59.10428
16 12.47697 59.25371
17 12.53677 59.40136
18 12.59657 59.54725
19 12.65636 59.69136
20 12.71616 59.83370
21 12.77596 59.97426
22 12.83576 60.11306
23 12.89556 60.25008
24 12.95535 60.38533
25 13.01515 60.51881
26 13.07495 60.65051
27 13.13475 60.78045
28 13.19455 60.90861
29 13.25434 61.03500
30 13.31414 61.15962
31 13.37394 61.28247
32 13.43374 61.40354
33 13.49354 61.52284
34 13.55333 61.64037
35 13.61313 61.75613
36 13.67293 61.87012
37 13.73273 61.98233
38 13.79253 62.09277
39 13.85232 62.20144
40 13.91212 62.30834
41 13.97192 62.41346
42 14.03172 62.51682
43 14.09152 62.61840
44 14.15131 62.71821
45 14.21111 62.81625
46 14.27091 62.91251
47 14.33071 63.00700
48 14.39051 63.09973
49 14.45030 63.19067
50 14.51010 63.27985
51 14.56990 63.36726
52 14.62970 63.45289
53 14.68949 63.53675
54 14.74929 63.61884
55 14.80909 63.69916
56 14.86889 63.77770
57 14.92869 63.85447
58 14.98848 63.92947
59 15.04828 64.00270
60 15.10808 64.07416
61 15.16788 64.14384
62 15.22768 64.21176
63 15.28747 64.27790
64 15.34727 64.34226
65 15.40707 64.40486
66 15.46687 64.46568
67 15.52667 64.52474
68 15.58646 64.58202
69 15.64626 64.63752
70 15.70606 64.69126
71 15.76586 64.74322
72 15.82566 64.79342
73 15.88545 64.84184
74 15.94525 64.88848
75 16.00505 64.93336
76 16.06485 64.97646
77 16.12465 65.01779
78 16.18444 65.05735
79 16.24424 65.09514
80 16.30404 65.13116
81 16.36384 65.16540
82 16.42364 65.19787
83 16.48343 65.22857
84 16.54323 65.25750
85 16.60303 65.28465
86 16.66283 65.31003
87 16.72263 65.33364
88 16.78242 65.35548
89 16.84222 65.37555
90 16.90202 65.39384
91 16.96182 65.41037
92 17.02162 65.42512
93 17.08141 65.43810
94 17.14121 65.44930
95 17.20101 65.45874
96 17.26081 65.46640
97 17.32061 65.47229
98 17.38040 65.47641
99 17.44020 65.47875
100 17.50000 65.47933sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn)) +
geom_point(colour="grey40")
sp + geom_line(data=predicted, size=1)
predictvals <- function(model, xvar, yvar, xrange=NULL, samples=100, ...) {
if (is.null(xrange)) {
if (any(class(model) %in% c("lm", "glm")))
xrange <- range(model$model[[xvar]])
else if (any(class(model) %in% "loess"))
xrange <- range(model$x)
}
newdata <- data.frame(x = seq(xrange[1], xrange[2], length.out = samples))
names(newdata) <- xvar
newdata[[yvar]] <- predict(model, newdata = newdata, ...)
newdata
}
modlinear <- lm(heightIn ~ ageYear, heightweight)
modloess <- loess(heightIn ~ ageYear, heightweight)
lm_predicted <- predictvals(modlinear, "ageYear", "heightIn")
loess_predicted <- predictvals(modloess, "ageYear", "heightIn")
sp + geom_line(data=lm_predicted, colour="red", size=.8) +
geom_line(data=loess_predicted, colour="blue", size=.8)
library(MASS)
b <- biopsy
b$classn[b$class=="benign"] <- 0
b$classn[b$class=="malignant"] <- 1
fitlogistic <- glm(classn ~ V1, b, family=binomial)
glm_predicted <- predictvals(fitlogistic, "V1", "classn", type="response")
ggplot(b, aes(x=V1, y=classn)) +
geom_point(position=position_jitter(width=.3, height=.08), alpha=0.4,
shape=21, size=1.5) +
geom_line(data=glm_predicted, colour="#1177FF", size=1)
Section 5.8. Adding Fitted Lines from Multiple Existing Models
make_model <- function(data) {
lm(heightIn ~ ageYear, data)
}
models <- dlply(heightweight, "sex", .fun = make_model)
predvals <- ldply(models, .fun=predictvals, xvar="ageYear", yvar="heightIn")
ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) +
geom_point() + geom_line(data=predvals)
predvals <- ldply(models, .fun=predictvals, xvar="ageYear", yvar="heightIn",
xrange=range(heightweight$ageYear))
ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) +
geom_point() + geom_line(data=predvals)
Section 5.9. Adding Annotations with Model Coefficients
model <- lm(heightIn ~ ageYear, heightweight)
summary(model)
Call:
lm(formula = heightIn ~ ageYear, data = heightweight)
Residuals:
Min 1Q Median 3Q Max
-8.3517 -1.9006 0.1378 1.9071 8.3371
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.4356 1.8281 20.48 <2e-16 ***
ageYear 1.7483 0.1329 13.15 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.989 on 234 degrees of freedom
Multiple R-squared: 0.4249, Adjusted R-squared: 0.4225
F-statistic: 172.9 on 1 and 234 DF, p-value: < 2.2e-16pred <- predictvals(model, "ageYear", "heightIn")
sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point() +
geom_line(data=pred)
sp + annotate("text", label="r^2=0.42", x=16.5, y=52)
sp + annotate("text", label="r^2 == 0.42", parse = TRUE, x=16.5, y=52)
eqn <- as.character(as.expression(
substitute(italic(y) == a + b * italic(x) * "," ~~ italic(r)^2 ~ "=" ~ r2,
list(a = format(coef(model)[1], digits=3),
b = format(coef(model)[2], digits=3),
r2 = format(summary(model)$r.squared, digits=2)
))))
sp + annotate("text", label=eqn, parse=TRUE, x=Inf, y=-Inf, hjust=1.1, vjust=-.5)
Section 5.10. Adding Marginal Rugs to a Scatterplot
ggplot(faithful, aes(x=eruptions, y=waiting)) + geom_point() + geom_rug()
ggplot(faithful, aes(x=eruptions, y=waiting)) + geom_point() +
geom_rug(position="jitter", size=.2)
Section 5.11. Labeling Point in a Scatterplot
sp <- ggplot(subset(countries, Year==2009 & healthexp>2000),
aes(x=healthexp, y=infmortality)) +
geom_point()
sp + annotate("text", x=4350, y=5.4, label="Canada") +
annotate("text", x=7400, y=6.8, label="USA")
sp + geom_text(aes(label=Name), size=4)
sp + geom_text(aes(label=Name), size=4, vjust=0)
sp + geom_text(aes(y=infmortality+.1, label=Name), size=4, vjust=0)
sp + geom_text(aes(label=Name), size=4, hjust=0)
sp + geom_text(aes(x=healthexp+100, label=Name), size=4, hjust=0)
cdat <- subset(countries, Year==2009 & healthexp>2000)
cdat$Name1 <- cdat$Name
idx <- cdat$Name1 %in% c("Canada", "Ireland", "United Kingdom", "United States",
"New Zealand", "Iceland", "Japan", "Luxembourg",
"Netherlands", "Switzerland")
idx [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
[12] TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[23] FALSE FALSE TRUE TRUE TRUEcdat$Name1[!idx] <- NA
ggplot(cdat, aes(x=healthexp, y=infmortality)) +
geom_point() +
geom_text(aes(x=healthexp+100, label=Name1), size=4, hjust=0) +
xlim(2000, 10000)
Section 5.12. Creating a Balloon Plot
cdat <- subset(countries, Year==2009 &
Name %in% c("Canada", "Ireland", "United Kingdom", "United States",
"New Zealand", "Iceland", "Japan", "Luxembourg",
"Netherlands", "Switzerland"))
p <- ggplot(cdat, aes(x=healthexp, y=infmortality, size=GDP)) +
geom_point(shape=21, colour="black", fill="cornsilk")
p
p + scale_size_area(max_size=15)
hec <- HairEyeColor[,,"Male"] + HairEyeColor[,,"Female"]
library(reshape2)
hec <- melt(hec, value.name="count")
ggplot(hec, aes(x=Eye, y=Hair)) +
geom_point(aes(size=count), shape=21, colour="black", fill="cornsilk") +
scale_size_area(max_size=20, guide=FALSE) +
geom_text(aes(y=as.numeric(Hair)-sqrt(count)/22, label=count), vjust=1,
colour="grey60", size=4)
Section 5.13. Making a Scatterplot Matrix
c2009 <- subset(countries, Year==2009,
select=c(Name, GDP, laborrate, healthexp, infmortality))
c2009 Name GDP laborrate healthexp
50 Afghanistan NA 59.8 50.88597
101 Albania 3772.6047 59.5 264.60406
152 Algeria 4022.1989 58.5 267.94653
203 American Samoa NA NA NA
254 Andorra NA NA 3089.63589
305 Angola 4068.5757 81.3 203.80787
356 Antigua and Barbuda 12501.8666 NA 652.53169
407 Argentina 7665.0734 65.0 730.17301
458 Armenia 2768.6126 66.3 128.85761
509 Aruba NA NA NA
560 Australia 42130.8203 65.2 3867.42853
611 Austria 45555.4345 60.4 5037.31089
662 Azerbaijan 4808.1688 63.0 284.72528
713 Bahamas, The 20916.2842 73.3 1557.65512
764 Bahrain 17608.8298 63.8 1107.90631
815 Bangladesh 607.7649 70.7 18.43125
866 Barbados 13181.3416 71.7 1041.44095
917 Belarus 5182.6304 60.2 294.61477
968 Belgium 43640.1962 53.5 5104.01899
1019 Belize 4056.1224 64.1 217.15214
1070 Benin 771.7088 72.7 31.92885
1121 Bermuda 88746.8944 NA NA
1172 Bhutan 1772.2838 62.7 97.98912
1223 Bolivia 1774.1952 71.9 84.78763
1274 Bosnia and Herzegovina 4523.3114 61.2 494.87951
1325 Botswana 5790.1819 76.6 611.92342
1376 Brazil 8251.0616 70.7 734.05138
1427 Brunei Darussalam 27390.0500 67.5 790.72871
1478 Bulgaria 6403.1477 54.5 474.84637
1529 Burkina Faso 509.2978 84.4 38.07546
1580 Burundi 162.8704 89.3 19.78891
1631 Cambodia 748.1512 79.3 42.10335
1682 Cameroon 1157.0245 67.0 61.05385
1733 Canada 39599.0418 67.8 4379.76084
1784 Cape Verde 3227.9520 66.4 146.10513
1835 Cayman Islands NA NA NA
1886 Central African Republic 458.5672 79.0 19.33792
1937 Chad 625.3019 70.4 41.78443
1988 Channel Islands NA NA NA
2039 Chile 9487.0111 57.3 787.19332
2090 China 3748.9344 73.7 177.14653
2141 Colombia 5165.7319 58.6 323.01081
2192 Comoros 747.9125 79.6 27.83652
2243 Congo, Dem. Rep. 174.5076 70.8 15.58425
2294 Congo, Rep. 2430.5255 72.7 70.07210
2345 Costa Rica 6369.1663 62.7 668.49256
2396 Cote d'Ivoire 1190.7895 66.9 55.25489
2447 Croatia 14322.6081 53.0 1120.37109
2498 Cuba NA 53.9 707.39670
2549 Curacao NA NA NA
2600 Cyprus 31298.4375 62.3 NA
2651 Czech Republic 18136.8451 57.9 1383.65144
2702 Denmark 55933.3545 65.4 6272.72868
2753 Djibouti 1202.9199 70.1 84.45174
2804 Dominica 5532.0537 NA 361.29188
2855 Dominican Republic 4756.3591 65.1 270.64760
2906 Ecuador 3647.6990 62.3 255.49979
2957 Egypt, Arab Rep. 2370.7111 48.8 113.29717
3008 El Salvador 3425.1706 59.9 228.57189
3059 Equatorial Guinea 17944.4045 65.5 709.40713
3110 Eritrea 364.2048 72.6 10.12132
3161 Estonia 14238.8817 61.2 1004.42550
3212 Ethiopia 393.6832 85.4 14.68076
3263 Faeroe Islands 45205.9305 NA NA
3314 Fiji 3314.2707 58.7 130.41002
3365 Finland 44576.7270 60.9 4309.60396
3416 France 40663.0502 56.1 4797.96556
3467 French Polynesia NA 57.3 NA
3518 Gabon 7411.1839 75.5 266.26454
3569 Gambia, The 436.1472 77.8 25.62880
3620 Georgia 2441.0105 63.7 255.62737
3671 Germany 40658.5823 59.8 4628.76920
3722 Ghana 1098.4257 74.6 45.05245
3773 Gibraltar NA NA NA
3824 Greece 28936.4809 53.7 3040.73383
3875 Greenland 22507.8887 NA NA
3926 Grenada 5906.4568 NA 446.52663
3977 Guam NA 64.4 NA
4028 Guatemala 2684.9664 66.9 186.12313
4079 Guinea 426.6530 84.2 18.77023
4130 Guinea-Bissau 562.4150 71.5 18.35889
4181 Guyana 2689.8559 63.5 132.50867
4232 Haiti 656.7792 69.9 39.60249
4283 Honduras 1921.8795 59.9 117.06353
4334 Hong Kong SAR, China 29881.8144 60.0 NA
4385 Hungary 12847.3031 50.1 937.98617
4436 Iceland 37972.2370 77.5 3130.39086
4487 India 1195.0003 57.6 44.80258
4538 Indonesia 2271.7753 68.9 55.44365
4589 Iran, Islamic Rep. 4525.9486 52.8 268.75470
4640 Iraq 2096.8511 41.3 98.45406
4691 Ireland 49737.9274 63.6 4951.84469
4742 Isle of Man NA NA NA
4793 Israel 26102.3506 57.1 1966.47189
4844 Italy 35073.3225 49.1 3327.62987
4895 Jamaica 4693.4275 64.7 231.08176
4946 Japan 39456.4388 59.5 3321.46639
4997 Jordan 4242.1537 49.3 335.81544
5048 Kazakhstan 7240.5703 70.6 330.11134
5099 Kenya 744.4031 82.2 33.24912
5150 Kiribati 1305.8038 NA 158.50643
5201 Korea, Dem. Rep. NA 66.0 NA
5252 Korea, Rep. 17109.9851 60.9 1107.94833
5303 Kosovo 3010.9934 NA NA
5354 Kuwait 41364.6893 68.5 1416.09990
5405 Kyrgyz Republic 881.3605 66.6 57.09366
5456 Lao PDR 997.2401 78.3 35.82477
5507 Latvia 11475.6923 61.5 750.44926
5558 Lebanon 8321.3707 46.1 663.27358
5609 Lesotho 800.4202 74.0 70.04993
5660 Liberia 229.2703 71.1 29.35613
5711 Libya 9957.4904 52.8 416.74524
5762 Liechtenstein 134914.6728 NA NA
5813 Lithuania 11033.5885 55.7 729.78492
5864 Luxembourg 106252.2442 55.5 8182.85511
5915 Macao SAR, China 40919.3262 69.4 NA
5966 Macedonia, FYR 4510.2380 54.0 313.68971
6017 Madagascar 421.7802 86.4 17.97023
6068 Malawi 327.3363 76.8 19.06556
6119 Malaysia 6908.6611 62.0 336.43858
6170 Maldives 4230.0510 67.1 330.69865
6221 Mali 601.2609 51.9 38.42789
6272 Malta 19326.4588 49.4 1446.43275
6323 Marshall Islands 2861.6376 NA 422.49589
6374 Mauritania 896.1959 70.0 21.91725
6425 Mauritius 6951.2787 57.5 383.05315
6476 Mayotte NA NA NA
6527 Mexico 7879.6773 61.4 514.80196
6578 Micronesia, Fed. Sts. 2498.2833 NA 336.91627
6629 Moldova 1525.5315 49.6 180.89118
6680 Monaco 172676.3407 NA 7137.38972
6731 Mongolia 1690.4170 72.9 74.19826
6782 Montenegro 6569.0869 NA 616.83557
6833 Morocco 2842.3235 52.3 155.67512
6884 Mozambique 428.1975 85.8 24.72483
6935 Myanmar NA 73.8 12.47223
6986 Namibia 4095.5044 57.1 257.96755
7037 Nepal 438.1784 71.5 25.34454
7088 Netherlands 48068.3540 66.1 5163.74038
7139 New Caledonia NA 57.0 NA
7190 New Zealand 29352.4540 68.6 2633.62461
7241 Nicaragua 1088.1658 62.4 104.69487
7292 Niger 351.2742 62.7 20.88920
7343 Nigeria 1091.1344 56.2 69.29737
7394 Northern Mariana Islands NA NA NA
7445 Norway 78408.7138 66.9 7661.60977
7496 Oman 17280.0972 55.7 496.64996
7547 Pakistan 950.1192 54.3 22.55684
7598 Palau 8094.5632 NA 980.54244
7649 Panama 6955.7448 64.6 590.72387
7700 Papua New Guinea 1180.6904 72.9 36.69268
7751 Paraguay 2245.3284 71.9 158.85965
7802 Peru 4412.3903 67.1 200.78948
7853 Philippines 1835.6365 63.8 66.88271
7904 Poland 11287.7389 53.7 803.64614
7955 Portugal 22029.8691 62.5 2409.66063
8006 Puerto Rico NA 46.1 NA
8057 Qatar 61531.6921 84.3 1714.99341
8108 Romania 7500.3404 52.4 408.32754
8159 Russian Federation 8614.6729 62.8 475.24572
8210 Rwanda 510.3116 86.0 48.18416
8261 Samoa 2721.9439 57.5 205.00032
8312 San Marino NA NA 4089.27133
8363 Sao Tome and Principe 1168.8878 59.8 90.73473
8414 Saudi Arabia 13900.6307 54.5 713.85023
8465 Senegal 1056.4957 76.4 58.90160
8516 Serbia 5689.8052 NA 419.03919
8567 Seychelles 9027.7301 NA 365.72463
8618 Sierra Leone 323.4532 66.4 43.85885
8669 Singapore 36757.5243 64.7 1501.33140
8720 Sint Maarten (Dutch part) NA NA NA
8771 Slovak Republic 16174.2284 59.5 1372.68818
8822 Slovenia 24101.2632 58.9 2174.71765
8873 Solomon Islands 1147.2437 37.5 71.91242
8924 Somalia NA 70.3 NA
8975 South Africa 5733.0410 55.0 485.43365
9026 South Sudan NA NA NA
9077 Spain 31891.3861 58.6 3075.01325
9128 Sri Lanka 2035.3030 54.2 83.61577
9179 St. Kitts and Nevis 10168.0093 NA 633.91741
9230 St. Lucia 5544.8437 63.0 442.57331
9281 St. Martin (French part) NA NA NA
9332 St. Vincent and the Grenadines 5357.2512 67.5 301.33061
9383 Sudan 1286.1473 52.3 94.59385
9434 Suriname 6255.2800 52.2 429.12629
9485 Swaziland 2512.9783 63.6 155.78038
9536 Sweden 43406.1748 64.8 4251.97636
9587 Switzerland 63524.6523 66.9 7140.72895
9638 Syrian Arab Republic 2691.5977 50.4 72.00606
9689 Tajikistan 733.8741 67.0 37.99971
9740 Tanzania 502.8518 88.4 25.31169
9791 Thailand 3838.2272 72.9 167.70008
9842 Timor-Leste 543.6922 71.0 73.24377
9893 Togo 534.8508 74.4 28.93053
9944 Tonga 3149.9735 64.6 161.18296
9995 Trinidad and Tobago 14684.0532 66.1 1069.21297
10046 Tunisia 4168.9509 48.0 239.96116
10097 Turkey 8553.7415 46.8 570.97184
10148 Turkmenistan 3710.4536 68.0 77.06955
10199 Turks and Caicos Islands NA NA NA
10250 Tuvalu 2609.9266 NA 290.02138
10301 Uganda 488.2459 84.5 42.54540
10352 Ukraine 2545.4803 58.1 179.56664
10403 United Arab Emirates 33183.1701 77.6 1520.05855
10454 United Kingdom 35163.4149 62.2 3285.05021
10505 United States 45744.5596 65.0 7410.16301
10556 Uruguay 9364.1241 64.1 698.16329
10607 Uzbekistan 1181.8601 64.6 62.15114
10658 Vanuatu 2635.3138 83.9 105.83174
10709 Venezuela, RB 11490.0291 66.0 686.34793
10760 Vietnam 1129.2889 71.9 79.71064
10811 Virgin Islands (U.S.) NA 59.5 NA
10862 West Bank and Gaza NA 42.8 NA
10913 Yemen, Rep. 1130.1833 46.8 64.00204
10964 Zambia 1006.3882 69.2 47.05637
11015 Zimbabwe 467.8534 66.8 NA
infmortality
50 103.2
101 17.2
152 32.0
203 NA
254 3.1
305 99.9
356 7.3
407 12.7
458 18.4
509 NA
560 4.2
611 3.6
662 41.1
713 14.0
764 8.9
815 40.0
866 17.1
917 4.5
968 3.6
1019 14.9
1070 74.7
1121 NA
1172 45.6
1223 43.4
1274 7.5
1325 37.1
1376 18.4
1427 5.9
1478 11.1
1529 93.3
1580 88.5
1631 45.6
1682 85.2
1733 5.2
1784 29.9
1835 NA
1886 106.8
1937 99.6
1988 NA
2039 7.7
2090 16.8
2141 17.1
2192 64.0
2243 112.8
2294 60.9
2345 8.8
2396 87.3
2447 4.9
2498 4.7
2549 NA
2600 3.3
2651 3.3
2702 3.4
2753 74.1
2804 11.5
2855 23.2
2906 18.2
2957 20.0
3008 15.0
3059 82.4
3110 43.7
3161 4.8
3212 69.5
3263 NA
3314 15.4
3365 2.5
3416 3.5
3467 NA
3518 55.2
3569 57.8
3620 20.5
3671 3.5
3722 51.3
3773 NA
3824 3.5
3875 NA
3926 9.4
3977 NA
4028 25.9
4079 83.6
4130 93.1
4181 26.2
4232 59.3
4283 21.2
4334 NA
4385 5.7
4436 1.7
4487 49.5
4538 28.1
4589 22.8
4640 31.9
4691 3.4
4742 NA
4793 3.7
4844 3.2
4895 20.7
4946 2.4
4997 18.9
5048 29.9
5099 56.3
5150 39.6
5201 26.4
5252 4.3
5303 NA
5354 9.6
5405 34.0
5456 44.3
5507 8.5
5558 19.4
5609 67.0
5660 77.6
5711 14.0
5762 1.8
5813 5.7
5864 2.2
5915 NA
5966 10.6
6017 44.9
6068 61.4
6119 5.6
6170 15.0
6221 100.6
6272 5.2
6323 22.8
6374 75.4
6425 13.2
6476 NA
6527 14.9
6578 34.9
6629 16.8
6680 3.4
6731 27.8
6782 7.5
6833 31.7
6884 94.6
6935 52.2
6986 31.6
7037 43.3
7088 3.8
7139 NA
7190 4.9
7241 23.7
7292 74.7
7343 90.4
7394 NA
7445 2.9
7496 8.4
7547 70.7
7598 15.1
7649 17.5
7700 47.7
7751 21.4
7802 16.1
7853 23.9
7904 5.4
7955 3.2
8006 NA
8057 7.0
8108 12.4
8159 9.8
8210 62.7
8261 17.5
8312 1.9
8363 53.6
8414 15.5
8465 50.8
8516 6.5
8567 11.7
8618 116.5
8669 2.1
8720 NA
8771 7.0
8822 2.5
8873 23.0
8924 108.3
8975 42.5
9026 NA
9077 4.0
9128 14.7
9179 7.0
9230 14.0
9281 NA
9332 19.2
9383 66.9
9434 27.3
9485 57.4
9536 2.4
9587 4.1
9638 14.3
9689 54.2
9740 52.5
9791 11.6
9842 49.2
9893 67.1
9944 13.8
9995 24.4
10046 14.8
10097 15.0
10148 48.0
10199 NA
10250 27.6
10301 65.0
10352 11.7
10403 6.4
10454 4.7
10505 6.6
10556 9.7
10607 44.5
10658 12.5
10709 16.1
10760 19.3
10811 NA
10862 20.7
10913 58.7
10964 71.5
11015 52.2panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y, use="complete.obs"))
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor * (1 + r) / 2)
}
panel.hist <- function(x, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks
nB <- length(breaks)
y <- h$counts
y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col="white", ...)
}
pairs(c2009[,2:5], upper.panel = panel.cor,
diag.panel = panel.hist,
lower.panel = panel.smooth)
panel.lm <- function (x, y, col = par("col"), bg = NA, pch = par("pch"),
cex = 1, col.smooth = "black", ...) {
points(x, y, pch = pch, col = col, bg = bg, cex = cex)
abline(stats::lm(y ~ x), col = col.smooth, ...)
}
pairs(c2009[,2:5], pch=".",
upper.panel = panel.cor,
diag.panel = panel.hist,
lower.panel = panel.lm)
END!